What are the top 10 industry sectors that are using data analytics in the world?

What are the top 10 industry sectors that are using data analytics in the world?

We offer several stand points, answering this question on quora.com

Jason T Widjaja, Data science team lead. MBA and MSc Business Analytics.

  1. The biggest by far – financial markets. Wherever there is an immediate and tangible payoff for analytics, there you will find the most cutting edge data analytics. Long before data science was in vogue, what we call data science today was in play in hedge funds.
  2. A close second, gambling and betting. The industry can be characterised by probability and payoffs, at scale.
  3. Insurance. The discipline of actuarial science is a specific application of data analytics to the measurement and pricing of disk. Product and customer analytics also apply.
  4. Retail banking. Financial institutions deploy a large spectrum of data analytics whose footprint is only increasing with time. While risk management may be the most obvious, customer analytics, product analytics and branch network analytics are all widely in use.
  5. Mining and resources. Mine site operations are extremely sophisticated, automated, and churn out terabytes of data daily. There is a broad range of analytics already in play or being implemented in large miners including smart exploration, yield optimisation, routing improvements, predictive asset management and many others across the industry value chain.
  6. Consumer products (Fast Moving Consumer Goods – FMCG). Data analytics in pricing and distribution across different brands, channels, and markets. Any time you have stepped into an expensive supermarket and found that your brand of milk is more expensive, data analytics has been at work.
  7. Healthcare and pharmaceutical. Data analytics works across the value chain, from drug discovery to clinical trials to manufacturing to sales and marketing.
  8. Energy. Sophistication varies by country, but generation, distribution and billing all requires data analytics to balance loads, price energy and manage infrastructure. In particular, smart meters and new alternate energy technologies promise to generate orders of magnitude more data in the very near future.
  9. E-commerce. Ever since the first ‘customers who bought item X also bought…’, using data analytics to make recommendations have been a feature in E-commerce. Methods can range from basic to extremely sophisticated, but data underpins it all.
  10. Web analytics – even without a product, there are vast amounts of data to scrape and analyse. Some of it has an unclear ROI (how much does engagement drive sales exactly?) but there is a massive number of tools analysing data from customer and social logins, as well as interactions such as how long a user spent on each page, where the user moved his/her mouse, how far down the page was scrolled and much, much more.

Alket Cecaj, As a co-author of scientific paper “Re-identification of Anonymized CDR datasets Using Social Network Data”

1-  Banking and trading! Financial  services in general.

2- Insurance industry

3- Manufacturing industry in general for supply chain managment and optimization.

4- IT sector in particular in Smart Cities context : that means also
4.1 –  smart mobility
4.2 – smart government
4.3 – smart economy
4.4-  smart environment
4.5- …….

5- Comunications sector specially mobile telecomunications.



About the author

Dr. Mariana Damova is the CEO of Mozaika, a company providing research and solutions in the field of data science, reasoning with natural language semantics, and natural human computer interfaces, creativity enhancing applications, and research infrastructures for the humanities. Previously, she was a Business development Manager and a Knowledge Management Expert specializing in ontology engineering and linked data management at a world leading technology provider. She was instrumental in the successful winning and knowledge modelling of large data integration and management projects such as the Semantic Knowledge Base for The National Archive of the United Kingdom and Research Space for the British Museum, as well as European FP7 projects such as Europeana Creative and Multisensor. Her work focuses on the design and development of data integration infrastructures which allow efficient querying, access and navigation over linked data. She has managed the building of the official experimental Europeana SPARQL endpoint holding Europeana semantic data. Mariana holds a PhD from the University of Stuttgart and teaches semantic technologies and multimedia at the New Bulgarian University in Sofia. She regularly reviews books and articles for ACM ComputingReviews.com and has authored books and scientific articles in linguistics and semantic technologies. She has successfully lead international interdisciplinary teams and projects carrying technological risks, driven and managed change in engineering and operational contexts in North America and in Europe, and acquired the ability to leverage marketing requirements with knowledge intense technological solutions.

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